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2015 IEEE Conference on Visual Analytics Science and Technology (VAST) (2015)
Chicago, IL, USA
Oct. 25, 2015 to Oct. 30, 2015
ISBN: 978-1-4673-9783-4
pp: 191-192
Shenghui Cheng , Visual Analytics and Imaging Lab, Computer Science Department, Stony Brook University and SUNY Korea
Yue Wang , Department of Computer Science, Shandong University, China
Dan Zhang , Visual Analytics and Imaging Lab, Computer Science Department, Stony Brook University and SUNY Korea
Zhifang Jiang , Department of Computer Science, Shandong University, China
Klaus Mueller , Visual Analytics and Imaging Lab, Computer Science Department, Stony Brook University and SUNY Korea
ABSTRACT
In streaming acquisitions the data changes over time. ThemeRiver and line charts are common methods to display data over time. However, these methods can only show the values of the variables (or attributes) but not the relationships among them over time. We propose a framework we call StreamVisND that can display these types of streaming data relations. It first slices the data stream into different time slices, then it visualizes each slice with a sequence of multivariate 2D data layouts, and finally it flattens this series of displays into a parallel coordinate type display. Our framework is fully interactive and lends itself well to real-time displays.
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CITATION
Shenghui Cheng, Yue Wang, Dan Zhang, Zhifang Jiang, Klaus Mueller, "StreamVisND: Visualizing relationships in streaming multivariate data", 2015 IEEE Conference on Visual Analytics Science and Technology (VAST), vol. 00, no. , pp. 191-192, 2015, doi:10.1109/VAST.2015.7347673
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